Information retrieval and management within a business or organizational enterprise typically presents numerous technical issues associated with data integration and interpretation, especially when the data originates from multiple sources. For example, difficulties frequently arise when attempting to unify and/or format large amounts of data for user visualization in terms of incurred administrative burden and complex programmatic logic. Additionally, integration of information from one or more sources configured to provide real-time data with other desired information presents accessibility, bandwidth, and latency problems limiting the flexibility and scalability of these systems as a whole.
A conventional computerized system associated with monitoring and controlling various operational parameters for components and sub-components of a manufacturing plant may be required to process large amounts of real-time and/or near real-time data. This data, referred to as point data, may arise from independent sources with each source configured to provide substantially “raw” or “native” information at pre-defined intervals such as numerical values associated with various gauge/monitor readings. Taken alone, this data may provide little or no context for its interpretation and require additional information to be associated with it to subsequently permit meaningful processing and analysis. It also may be desirable to capture, store and distribute the point data to other processing components requiring some degree of context be ascribed to the point data.
Modern industrial automation systems, supervisory control and data acquisition (SCADA) systems, general data acquisition systems and plant data historians represent but a few of the classes or categories of systems that may store and distribute real-time and/or near real-time information in the form of point data. In such systems, a Tag represents a structural data element associated with point data arising from various components of the system which are made accessible to other components, systems, applications and/or users. In general, point data is subject to dynamic change and is monitored, and reported through various operations and functions associated with processing the point data obtained from selected sources. In industrial automation and control systems, decision support and reporting capabilities may be provided based on Tag associated point data that is monitored over very short timeframes ranging in the sub-second to sub-minute range.
A limitation found in many conventional systems is that they provide only limited capabilities to access, interpret, and/or manipulate tag-based point data collectively or in connection with other non-point data. Non-point data relates to a broad category of context-providing information associated with point data that, in one sense, extend the functionality and meaning of the point data. Non-point data may include descriptive and/or attribute information characterizing the point data, as well as, other information such as limits, ranges, etc. In conventional systems, integral and flexible manipulation of tag-based point data and non-point data is restricted due to their inherent differences and properties.
A further difficulty encountered in conventional systems is the limited ability to integrate and relate tag-based data and non-tag-based data. Non-tag-based data may originate from numerous sources and relate to disparate aspects of an enterprise environment. For example, non-tag-based data may comprise data associated with conventional database applications/environments and include transactional information, production data, business data, etc. Conventionally, attempts to integrate non-tag-based data with tag-based information may be hindered or prevented completely as a consequence of the underlying differences in structure and content between these data types. As a result, generating and implementing logical constructions or schema in which both tag-based data and non-tag-based data are integrally used is problematic in conventional systems. Such limitations limit the overall flexibility of the system and increase the difficulty of scaling such systems to complex, enterprise-level environments.
Another important consideration to the integral management of point data and non-point data relates to the recognition of differences in desirable update or acquisition frequencies. The dynamic properties of point data give rise to time critical retrieval restrictions on systems designed to acquire and evaluate point data. Rapidly changing point data is generally acquired or refreshed at a high frequency (e.g. short retrieval time interval) to insure that the information is up-to-date. Other point data and non-point data information may be more static in nature and not require a similar short acquisition interval. Developing efficient and customizable data acquisition strategies for information retrieval which take into account data characteristics and optimal acquisition rates is important to insure accuracy and timeliness in the data without imparting undue computational or transmission load.
Conventional systems are not well suited to provide integration of customizable data-dependent acquisition strategies or associated acquisition rates. As a result, these systems experience reduced performance, especially in complex environments where data or values to be retrieved possess different optimal or desired refresh rates. Furthermore, these conventional systems fail to provide the ability to easily customize or configure differential acquisition strategies for point data and non-point data in such a manner so as to improve overall system performance. Consequently, there is a need to overcome these limitations integrating and managing tag-based information to provide improved mechanisms to associate and work with point data and non-point data within the same programmatic or logical environment.